CN112828311B - Metal additive manufacturing online track adjusting method based on real-time three-dimensional detection - Google Patents

Metal additive manufacturing online track adjusting method based on real-time three-dimensional detection Download PDF

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CN112828311B
CN112828311B CN202110000780.XA CN202110000780A CN112828311B CN 112828311 B CN112828311 B CN 112828311B CN 202110000780 A CN202110000780 A CN 202110000780A CN 112828311 B CN112828311 B CN 112828311B
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闵晓潮
党仁俊
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Suzhou Xingxiangyi Precision Manufacturing Co ltd
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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B33ADDITIVE MANUFACTURING TECHNOLOGY
    • B33YADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
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Abstract

The invention provides a metal additive manufacturing online track adjusting method based on real-time three-dimensional detection, and belongs to the technical field of additive manufacturing detection. The method comprises the steps of firstly installing a real-time three-dimensional detection device on the additive manufacturing equipment, carrying out off-line planning on a printing track, then measuring three-dimensional shape information of a transition molten pool, a local newly-generated surface and a local base material surface through the real-time three-dimensional detection device after manufacturing is started, converting the three-dimensional shape information into a correction amount of a current off-line planning track through calculation, superposing and compensating the correction amount to the current off-line planning track, and continuously adjusting the current off-line planning track until additive manufacturing is completed. The method is based on the mature real-time three-dimensional detection technology, simultaneously considers the features of the substrate surface, the molten pool, the newly generated surface and the like, establishes a double closed-loop control system to adjust the printing track on line, has good stability and high precision, and can effectively improve the metal additive manufacturing and forming quality.

Description

Metal additive manufacturing online track adjusting method based on real-time three-dimensional detection
Technical Field
The invention belongs to the technical field of additive manufacturing detection, and particularly provides a metal additive manufacturing online track adjusting method based on real-time three-dimensional detection.
Background
The metal additive manufacturing technology is a technology which takes powder or wire materials as raw materials, based on the discrete/accumulation principle, uses laser, electric arc or electron beam as a heat source to melt/solidify, and directly manufactures a metal solid three-dimensional component by a digital three-dimensional model. Compared with the traditional material reduction manufacturing, the technology effectively overcomes the defects of long manufacturing period, low material utilization rate and the like of the complex component, and is more suitable for manufacturing large complex components such as aerospace, automobiles, medical appliances and the like. However, in the existing additive manufacturing process, the process is very complex, especially in the metal melting process, the forming quality is very sensitive to the surface of the substrate and the molten pool, and meanwhile, the new surface is also an important reference for forming precision feedback, and the factors need to be considered at the same time to perform online adjustment on important parameters such as the feeding amount, the temperature, especially the printing track and the like.
However, in the existing research, the most mature online monitoring based on the visual imaging technology mainly focuses on online detection of the physical and dimensional parameters of the molten pool, namely the width and the height, establishment of feedback control, adjustment of the printing track and improvement of the forming quality. In other forms of online detection technologies, such as online detection technologies based on spectrum, temperature, sound or electrical signals, part of the application research of the technologies is still in the starting stage, and the other part of the technologies obtains less information and can only be used as an assistant.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a metal additive manufacturing online track adjusting method based on real-time three-dimensional detection. The method is based on the mature real-time three-dimensional detection technology, simultaneously considers the features of the substrate surface, the molten pool, the newly generated surface and the like, establishes a double closed-loop control system to adjust the printing track on line, has good stability and high precision, and can effectively improve the metal additive manufacturing and forming quality.
The invention provides a metal additive manufacturing online track adjusting method based on real-time three-dimensional detection, which is characterized by comprising the following steps of:
1) the real-time three-dimensional detection device is arranged at a transmission device of a printing head of the metal additive manufacturing equipment or at a fixed position in the printing machine, so that the detection range of the real-time three-dimensional detection device covers the lower part and the peripheral area of the printing head, and the printing head is avoided;
2) carrying out three-dimensional modeling on a metal component to be manufactured to obtain a three-dimensional model of the metal component, then layering the three-dimensional model to obtain a corresponding layering result, setting a growth direction and printing parameters, planning to obtain an initial offline printing trajectory, and taking the initial offline printing trajectory as a current offline planning trajectory gamma plan
3) Planning the trajectory gamma according to the current off-lineplanPerforming additive manufacturing on a metal component to be manufactured, and planning a trajectory gamma according to the current off-lineplanDuring the manufacturing process, the corresponding transition weld pool P is formed in the printing head regionFTransition molten pool PFLocal new surfaces S are respectively formed around1And S1Local substrate surface S formed after delay time tau2
4) Using real time threeDimension detection device for collecting transition molten pool PFLocal fresh noodle S1And local substrate surface S2Three-dimensional shape information of;
5) the transition molten pool P collected according to the step 4)FAnd local substrate surface S2Establishing and calculating a first adjustment quantity delta gamma1Model1 of (1); the method comprises the following specific steps:
5-1) defining a current offline planning trajectory gammaplanOn the local substrate surface S2Each point on the table is a current off-line planning track point pi,piNeighborhood of (2)
Figure BDA0002881335580000021
From point ptConstitution ptNormal to the surface of ntThen p isiNeighborhood of (2)
Figure BDA0002881335580000022
The expression of (a) is as follows:
Figure BDA0002881335580000023
wherein, | | pt-piI represents the point ptAnd point piOn the local substrate surface S2A distance of (d) is a point ptAnd point piOn the local substrate surface S2The maximum distance of (a);
the local substrate surface S acquired according to the step 4)2Using a surface processing algorithm to calculate each piCorresponding ptAnd nt
5-2) the transition zone molten pool P collected according to step 4) FExtracting a molten pool P in a transition region according to a shape extraction algorithmFIncluding the center of the molten pool
Figure BDA0002881335580000024
Long shaft
Figure BDA0002881335580000025
Short shaft
Figure BDA0002881335580000026
And the angle of inclination
Figure BDA0002881335580000027
5-3) according to a single-droplet structure model MsbModel M with multiple molten dropsmbAll of p obtained in step 5-1)tCorresponding surface normal ntMajor axis of molten pool obtained in step 5-2)
Figure BDA0002881335580000028
Short shaft
Figure BDA0002881335580000029
And the angle of inclination
Figure BDA00028813355800000210
Calculating a first attitude correction amount of the print head
Figure BDA00028813355800000211
Wherein
Figure BDA00028813355800000212
First rotation angle correction quantities of the printing head in the x, y and z directions respectively;
5-4) according to the single-droplet structure model MsbModel M with multiple molten dropsmbAll p obtained in step 5-2)tAnd the center of the molten pool obtained in the step 5-2)
Figure BDA0002881335580000031
Calculating a first displacement correction amount Deltav of the print head1=[Δx1,Δy1,Δz1]TWherein Δ x1,Δy1,Δz1First displacement correction amounts of the printing head in x, y and z directions respectively;
5-5) obtaining a first attitude correction quantity Δ ω according to step 5-3)1And the first displacement correction amount Deltav obtained in step 5-4)1Establishing and calculating a first adjustment quantity delta gamma1Model1, and calculates a first adjustment amount Δ Γ1Wherein the Model1 expression is:
Figure BDA0002881335580000032
6) the local new surface S collected according to the step 4)1Establishing and calculating a second adjustment quantity delta gamma2Model2 of (1); the method comprises the following specific steps:
6-1) collecting the local new surface S according to the step 4)1Three-dimensional topography information of S1As the current local new surface, based on the Markov process, a probability model for estimating the local new surface at the next moment according to the current local new surface is established to obtain the local new surface at the next moment
Figure BDA0002881335580000033
Three-dimensional topography information;
6-2) extracting and obtaining the expected local new surface of the current printing head position at the next moment according to the three-dimensional model and the layering result of the metal component obtained in the step 2)
Figure BDA0002881335580000034
Three-dimensional shape information of;
6-3) the local new noodle at the next moment
Figure BDA0002881335580000035
And the next moment when local new noodles are expected
Figure BDA0002881335580000036
Comparing to obtain a difference value
Figure BDA0002881335580000037
6-4) according to Δ S1Single-molten-drop structure model MsbAnd a multiple droplet overlay model MmbCalculating a second attitude correction amount of the print head
Figure BDA0002881335580000038
And a second displacement correction amount Deltav2=[Δx2,Δy2,Δz2]TWherein
Figure BDA0002881335580000039
Second angular corrections, Δ x, of the print head in the x, y, z directions, respectively2,Δy2,Δz2Second displacement correction amounts of the printing head in x, y and z directions respectively;
6-5) according to Δ S1、Δω2And Δ v2Establishing and calculating the second adjustment quantity delta gamma2Model2, and calculating a second adjustment amount Δ Γ2Wherein the Model2 expression is:
Figure BDA0002881335580000041
7) for the first adjustment quantity delta gamma1And a second adjustment amount DeltaGamma2And superposing to obtain a total track adjustment quantity delta gamma, and then compensating the delta gamma to the current offline planning track gamma planObtaining the track gamma after on-line adjustmentonlineThe specific calculation method comprises the following steps:
Figure BDA0002881335580000042
Figure BDA0002881335580000043
wherein,
Figure BDA0002881335580000044
representing a superposition compensation operation;
8) gamma-gamma is formedonlineAs new gammaplanAnd then returning to the step 3) again to perform a new round of off-line planning track adjustment until the whole metal component to be manufactured is manufactured.
The invention has the characteristics and beneficial effects that:
according to the metal additive manufacturing online track adjusting method based on real-time three-dimensional detection, a double closed-loop control system is established to adjust the printing track online by utilizing a mature real-time three-dimensional detection technology and considering the features of the substrate surface, the molten pool, the newly generated surface and the like, so that the stability is good, the precision is high, and the metal additive manufacturing molding quality can be effectively improved.
When the real-time three-dimensional detection result is processed, the problem of sparse or singular noise and the like caused by light reflection of the forming surface and shielding of partial area is solved, the stability of online control is improved, and a tensor voting frame is adopted to process the point cloud result.
The invention simultaneously considers the influence of a new surface, a molten pool and a base material surface on additive manufacturing, adopts two models to calculate the online track adjustment amount, forms a double closed-loop control system, and effectively improves the stability and the precision of the printing track.
The invention can be used for online adjustment of printing tracks in metal additive manufacturing, can be used as an auxiliary means in the production and manufacturing process, and has an important effect on improving the product quality.
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Fig. 1 is an overall flowchart of a metal additive manufacturing online trajectory adjustment method based on real-time three-dimensional detection according to the present invention.
Fig. 2 is a schematic diagram of a real-time three-dimensional detection device and a metal additive manufacturing apparatus according to an embodiment of the invention.
Fig. 3 is a flowchart of calculating a first adjustment amount in the present invention.
Fig. 4 is a flowchart of calculating the second adjustment amount in the present invention.
In the figure, 1 is a projector, 2 is a camera, 3 is a print head, 4 is a console, 5 is a rigid frame, and 6 is a current off-line planned trajectory ΓplanAnd 7 is a transition molten pool P F8 is a local fresh noodle S1And 9 is a partial substrate surface S2And 10 is an on-line adjusted trajectory gammaonline
Detailed Description
The invention provides a metal additive manufacturing online track adjusting method based on real-time three-dimensional detection, and the invention is further described in detail below with reference to the accompanying drawings and specific embodiments. The following examples are intended to illustrate the invention, but are not intended to limit the scope of the invention.
The invention provides a metal additive manufacturing online track adjusting method based on real-time three-dimensional detection, and the overall flow is as shown in figure 1, and the method comprises the following steps:
S1: installing a real-time three-dimensional detection device;
the method comprises the steps that a real-time three-dimensional detection device is arranged at a reasonable position in a transmission device of a printing head of metal additive manufacturing equipment or a printer, so that the real-time three-dimensional detection device is ensured not to interfere with a normal additive manufacturing process, and the additive manufacturing process also does not interfere with the detection of the real-time three-dimensional detection device, on the basis, the detection range of the real-time three-dimensional detection device is ensured to cover the lower part and the peripheral area of the printing head, and the printing head is avoided;
in the present invention, the metal additive manufacturing apparatus includes, but is not limited to, various types of additive manufacturing apparatuses based on existing mature technologies, such as laser, electron beam, plasma, arc, etc., or based on new technologies that may emerge in the future. The real-time three-dimensional detection device includes, but is not limited to, various real-time three-dimensional detection devices based on existing mature technologies, such as an extended stereo vision method, a passive stereo vision method, a time-of-flight method, a defocusing method, a structured light projection method, and the like, or based on new technologies that may appear in the future. The peripheral area generally refers to a rectangular area with a printing point of additive manufacturing equipment as a center, wherein the long edge of the rectangle is parallel to a printing track, the short edge of the rectangle is perpendicular to the printing track, the length of the short edge of the rectangle is generally 5-10 cm, the rest distances are also available, the length of the long edge depends on the balance of precision and speed, if the long edge is longer, the track adjustment precision is higher, but the printing time is slowed down due to more processing data, otherwise, the printing time is shortened, but the track adjustment precision is reduced, and from the balance of the two, the distance is generally 15-25 cm, and the rest distances are also available.
As shown in fig. 2, in this embodiment, the real-time three-dimensional detection device selects a detection device based on a structured light projection method, which includes: the projector comprises a projector 1, a camera 2 and a computer, wherein the projector 1 and the camera 2 are respectively connected with the computer. The metal additive manufacturing equipment adopts an electron beam fuse deposition equipment EBAM 110 system of Sciaky company, and comprises a printing head 3, an operating platform 4, an industrial personal computer and the like, wherein the printing head 3 and the operating platform 4 are respectively connected with the industrial personal computer. And the computer of the real-time three-dimensional detection device is connected with the industrial personal computer of the metal additive manufacturing equipment, and the computer is used for controlling the real-time three-dimensional detection device and the metal additive manufacturing equipment respectively. The projector 1 and the camera 2 are respectively installed on two sides of the printing head 3, the main shafts of the projector 1, the camera 2 and the printing head are fixedly connected through bolts by using a steel rigid frame 5, a rectangular area with a detection range of 10cm width and 20cm length covers the area below and nearby the printing head, and the long side of the rectangle is parallel to the advancing track of the printing head 3.
S2: planning a printing track off line;
carrying out three-dimensional modeling on a metal component to be manufactured to obtain a three-dimensional model of the metal component, then layering the three-dimensional model to obtain a corresponding layering result, setting a growth direction and printing parameters, planning to obtain an initial offline printing track, and taking the initial offline printing track as a current offline planning track gamma plan
In this embodiment, the three-dimensional modeling software is Solidworks, and operations such as layering and planning are executed by software built in the EBAM 110 industrial personal computer. Current offline planned trajectory Γ plan6 as shown in fig. 2, depicted using dashed lines, the arrows point in the direction of travel.
S3: planning the trajectory gamma according to the current off-lineplanPerforming additive manufacturing on a metal component to be manufactured, and planning a trajectory gamma according to the current off-lineplanDuring the manufacturing process, the corresponding transition weld pool P is formed in the printing head regionFTransition molten pool PFLocal new surfaces S are respectively formed around1And S1Local substrate surface S formed after delay time tau2
As shown in FIG. 2, in this embodiment, the printhead 3 follows a current offline planned trajectory Γ plan6 go and executeCorresponding to the manufacturing process, a transition molten pool 7 (shown as a circle portion below 3 in fig. 2), a local newly formed surface 5 (shown as a diamond grid portion in fig. 2), and a local base material surface 6 (shown as a square grid portion in fig. 2) are formed on the work table 4.
S4: acquisition of transition molten pool P using real-time three-dimensional detection deviceFLocal fresh noodle S1And local substrate surface S2Three-dimensional shape information of;
in the present invention, three-dimensional topography information refers to all expression forms capable of representing information of an object in a three-dimensional space, including but not limited to three-dimensional depth maps, three-dimensional voxel volumes, three-dimensional polygonal meshes, and three-dimensional point clouds.
The embodiment uses a structured light four-step phase shift method and three-dimensional point cloud to transition molten pool PFLocal fresh noodle S1And local substrate surface S2Measuring and expressing, projecting different coding patterns to the detection range by the projector 1, collecting the coding patterns by the camera 2, and processing to obtain a molten pool P containing transition F7. Local new noodle S 18 and partial substrate surface S 29, further processing and dividing the three-dimensional point cloud in the rectangular area inside the rectangular area according to the height and the normal direction to obtain a transition molten pool P F7. Local new noodle S 18 and partial substrate surface S 29 independent three-dimensional point clouds.
S5: transition weld pool P collected according to S4FAnd local substrate surface S2Establishing and calculating a first adjustment quantity delta gamma1Model1 of (1);
the overall flow of the steps is shown in fig. 3, and the specific steps are as follows:
s51, defining the current off-line planning trajectory gammaplanOn the local substrate surface S2Each point on the table is a current off-line planning track point pi,piNeighborhood of (2)
Figure BDA0002881335580000071
From point ptComposition of ptNormal to the surface of ntThen p isiNeighborhood A ofpiIs defined as:
Figure BDA0002881335580000072
wherein, | | pt-piI represents the point ptAnd point piOn the local substrate surface S2A distance of (d) is a set point ptAnd point piOn the local substrate surface S2Should be less than p tWith local substrate surface S2The maximum distance of the boundary;
partial substrate surface S acquired according to step S42Using a surface processing algorithm to calculate each p according to the specified deltaiCorresponding ptAnd ntWherein p istAnd ntIn a one-to-one correspondence, and for repeated ptOnly once calculated; s52: the transition zone molten pool P collected according to the step S4FExtracting the molten pool P of the transition region according to a shape extraction algorithmFIncluding the center of the molten pool
Figure BDA0002881335580000073
Long shaft
Figure BDA0002881335580000074
Short shaft
Figure BDA0002881335580000075
And the angle of inclination
Figure BDA0002881335580000076
S53: according to a single droplet structure model MsbModel M with multiple molten dropsmbAll p obtained in step S51tCorresponding surface normal ntMajor axis of molten pool obtained in step S52
Figure BDA0002881335580000077
Short shaft
Figure BDA0002881335580000078
And the angle of inclination
Figure BDA0002881335580000079
Calculating a first attitude correction amount of the print head
Figure BDA00028813355800000710
Wherein
Figure BDA00028813355800000711
First rotation angle correction quantities of the printing head in the x, y and z directions respectively;
s54: according to a single droplet structure model MsbModel M with multiple molten dropsmbAll p obtained in step S52tAnd the center of the molten pool obtained in step S52
Figure BDA00028813355800000712
Calculating a first displacement correction amount Deltav of the print head1=[Δx1,Δy1,Δz1]TWherein Δ x1,Δy1,Δz1The first displacement correction amounts of the print head in the x, y, z directions, respectively.
S55: the first posture correction amount Δ ω obtained in step S53 1And the first displacement correction amount Δ v obtained in step S541Establishing and calculating a first adjustment quantity delta gamma1Model1, and calculates a first adjustment amount Δ Γ1Wherein the Model1 is:
Figure BDA00028813355800000713
in the invention, a curved surface processing algorithm and a morphology extraction algorithm refer to algorithms capable of achieving the aim of extracting information, and the algorithms are influenced by the three-dimensional information expression form, for example, when a three-dimensional depth map is adopted to express morphology information, a shadow recovery method and a stereo matching method can be adopted, and when three-dimensional point cloud is adopted to express morphology information, an OPA algorithm, a neural network method and the like can be adopted.
In this embodimentDefining delta to be 0.01m, and processing the local substrate surface S by using a tensor voting frame method and a tensor space surface normal estimation method 26 point cloud space, calculating point ptAnd its surface normal nt. Then from the transition bath PFThe three-dimensional point cloud is calculated and obtained by using a contour interactive drawing algorithm
Figure BDA0002881335580000081
Long shaft
Figure BDA0002881335580000082
Short shaft
Figure BDA0002881335580000083
And the angle of inclination
Figure BDA0002881335580000084
Each binding ntAnd ptAccording to a single droplet structure model MsbAnd a multiple droplet overlay model MmbCalculating a first attitude correction
Figure BDA0002881335580000085
And a first displacement correction amount Deltav1=[Δx1,Δy1,Δz1]T. Finally, a first adjustment amount delta gamma is calculated according to the first posture correction amount and the first displacement correction amount and the Model1 1
S6: local fresh surface S collected according to S41Establishing and calculating a second adjustment quantity delta gamma2Model2 of (1).
The overall flow of the steps is shown in fig. 4, and the specific steps are as follows:
s61: local new surface S collected according to step S41Three-dimensional topography information of S1As the current local new surface, based on the Markov process, a probability model for estimating the local new surface at the next moment according to the current local new surface is established to obtain the local new surface at the next moment
Figure BDA0002881335580000086
Three-dimensional topography information;
s62, extracting the expected local new surface at the next moment of the current printing head position according to the three-dimensional model of the metal member and the layering result obtained in the step S2
Figure BDA0002881335580000087
Three-dimensional shape information of;
s63: the local new noodle at the next moment
Figure BDA0002881335580000088
And the next moment when local new noodles are expected
Figure BDA0002881335580000089
Comparing to obtain a difference value
Figure BDA00028813355800000810
S64: according to Δ S1Single-molten-drop structure model MsbAnd a multiple droplet overlay model MmbCalculating a second attitude correction amount of the print head
Figure BDA00028813355800000811
And a second displacement correction amount Deltav2=[Δx2,Δy2,Δz2]TWherein
Figure BDA00028813355800000812
Second angular corrections, Δ x, of the print head in the x, y, z directions, respectively2,Δy2,Δz2Second displacement correction amounts of the printing head in x, y and z directions respectively;
s65: according to Δ S1、Δω2And Δ v2Establishing and calculating the second adjustment quantity delta gamma 2Model2, and calculating a second adjustment quantity delta gamma2Wherein the Model2 is:
Figure BDA0002881335580000091
in this embodiment, a Markov process is first utilized, based on the local new surface S1Estimating the local new surface of the next moment by the three-dimensional point cloud
Figure BDA0002881335580000092
And the expectation of local new surface
Figure BDA0002881335580000093
Comparing the three-dimensional point clouds to obtain difference point clouds
Figure BDA0002881335580000094
According to a single droplet structure model MsbAnd a multiple droplet overlay model MmbIn combination with Δ S1Calculating a second attitude correction
Figure BDA0002881335580000095
And a second displacement correction amount Deltav2=[Δx2,Δy2,Δz2]T. Finally, a second adjustment quantity delta gamma is calculated according to the second posture correction quantity and the second displacement correction quantity and the Model22
S7: for the first adjustment quantity delta gamma1And a second adjustment amount DeltaGamma2And superposing to obtain a total track adjustment quantity delta gamma, and then compensating the delta gamma to the current offline planning track gammaplanObtaining the track gamma after on-line adjustmentonlineThe specific calculation method comprises the following steps:
Figure BDA0002881335580000096
Figure BDA0002881335580000097
wherein,
Figure BDA0002881335580000098
indicating a superposition compensation operation.
This exampleFirst, the first adjustment amount Δ Γ obtained in step S5 is adjusted1The second adjustment amount Δ Γ obtained in step S6 is compensated for by superposition2Obtaining the total track adjustment quantity delta gamma, and then superposing and compensating the total track adjustment quantity delta gamma to the offline track gammaplan6 (dotted line, arrow indicating print advance direction), the trajectory Γ after the in-line adjustment is obtained online10 (shown by a dot-dash line, the arrow indicates the print travel direction), which is a function of the offline trajectory Γ plan6 different print trajectories.
S8: will gammaonlineAs new gammaplanAnd then returning to the step S3 again to perform a new round of off-line planned trajectory adjustment until the whole metal member to be manufactured is manufactured.

Claims (2)

1. A metal additive manufacturing online track adjusting method based on real-time three-dimensional detection is characterized by comprising the following steps:
1) the real-time three-dimensional detection device is arranged at a transmission device of a printing head of the metal additive manufacturing equipment or at a fixed position in the printing machine, so that the detection range of the real-time three-dimensional detection device covers the lower part and the peripheral area of the printing head, and the printing head is avoided;
2) carrying out three-dimensional modeling on a metal component to be manufactured to obtain a three-dimensional model of the metal component, then layering the three-dimensional model to obtain a corresponding layering result, setting a growth direction and printing parameters, planning to obtain an initial offline printing trajectory, and taking the initial offline printing trajectory as a current offline planning trajectory gammaplan
3) Planning a trajectory gamma according to the current off-lineplanPerforming additive manufacturing on a metal component to be manufactured, and planning a trajectory gamma according to the current off-line planDuring the manufacturing process, the corresponding transition melting pool P is formed in the printing head areaFTransition molten pool PFLocal new surfaces S are respectively formed around1And S1Local substrate surface S formed after delay time tau2
4) Acquisition of transition molten pool P using real-time three-dimensional detection deviceFLocal fresh noodle S1Office of harmonyPart of the base material surface S2Three-dimensional shape information of;
5) the transition molten pool P collected according to the step 4)FAnd local substrate surface S2Establishing and calculating a first adjustment quantity delta gamma1Model1 of (1); the method comprises the following specific steps:
5-1) defining a current offline planning trajectory gammaplanOn the local substrate surface S2Each point on the table is a current off-line planning track point pi,piNeighborhood of (2)
Figure FDA0002881335570000011
From point ptConstitution ptNormal to the surface of ntThen p isiNeighborhood of (2)
Figure FDA0002881335570000012
The expression of (a) is as follows:
Figure FDA0002881335570000013
wherein, | | pt-piI represents the point ptAnd point piOn the local substrate surface S2A distance of (d) is a point ptAnd point piOn the local substrate surface S2The maximum distance of (a);
the local substrate surface S acquired according to the step 4)2Using a surface processing algorithm to calculate each piCorresponding ptAnd nt
5-2) the transition zone molten pool P collected according to step 4)FExtracting the molten pool P of the transition region according to a shape extraction algorithmFIncluding the center of the molten pool
Figure FDA0002881335570000014
Long shaft
Figure FDA0002881335570000015
Short shaft
Figure FDA0002881335570000016
And the angle of inclination
Figure FDA0002881335570000017
5-3) according to a single-droplet structure model MsbModel M with multiple molten dropsmbAll of p obtained in step 5-1)tCorresponding surface normal ntMajor axis of molten pool obtained in step 5-2)
Figure FDA0002881335570000021
Short shaft
Figure FDA0002881335570000022
And the angle of inclination
Figure FDA0002881335570000023
Calculating a first attitude correction amount of the print head
Figure FDA0002881335570000024
Wherein
Figure FDA0002881335570000025
First rotation angle correction quantities of the printing head in the x, y and z directions respectively;
5-4) according to the single-droplet structure model MsbModel M with multiple molten dropsmbAll p obtained in step 5-2)tAnd the center of the molten pool obtained in the step 5-2)
Figure FDA0002881335570000026
Calculating a first displacement correction amount Deltav of the print head1=[Δx1,Δy1,Δz1]TWherein Δ x1,Δy1,Δz1First displacement correction amounts of the printing head in x, y and z directions respectively;
5-5) obtaining a first attitude correction quantity Δ ω according to step 5-3)1And the first displacement correction amount Deltav obtained in step 5-4)1Establishing and calculating a first adjustment quantity delta gamma1Model1, and calculates a first adjustment amount Δ Γ1Wherein the Model1 expression is:
Model1:
Figure FDA0002881335570000027
6) the local new surface S collected according to the step 4)1Establishing and calculating a second adjustment quantity delta gamma2Model2 of (1); the method comprises the following specific steps:
6-1) collecting the local new surface S according to the step 4)1Three-dimensional topography information of S1As the current local new surface, based on the Markov process, a probability model for estimating the local new surface at the next moment according to the current local new surface is established to obtain the local new surface at the next moment
Figure FDA0002881335570000028
Three-dimensional topography information;
6-2) extracting and obtaining the expected local new surface of the current printing head position at the next moment according to the three-dimensional model and the layering result of the metal component obtained in the step 2)
Figure FDA0002881335570000029
Three-dimensional shape information of;
6-3) the local new noodle at the next moment
Figure FDA00028813355700000210
And the next moment when local new noodles are expected
Figure FDA00028813355700000211
Comparing to obtain a difference value
Figure FDA00028813355700000212
6-4) according to Δ S1Single-molten drop structure modelMsbAnd a multiple droplet overlay model MmbCalculating a second attitude correction amount of the print head
Figure FDA0002881335570000031
And a second displacement correction amount Deltav2=[Δx2,Δy2,Δz2]TWherein
Figure FDA0002881335570000032
Second angular corrections, Δ x, of the print head in the x, y, z directions, respectively2,Δy2,Δz2Second displacement correction amounts of the printing head in x, y and z directions respectively;
6-5) according to Δ S1、Δω2And Δ v2Establishing and calculating the second adjustment quantity delta gamma2Model2, and calculating a second adjustment amount Δ Γ2Wherein the Model2 expression is:
Figure FDA0002881335570000033
7) for the first adjustment quantity delta gamma1And a second adjustment amount DeltaGamma2And superposing to obtain a total track adjustment quantity delta gamma, and then compensating the delta gamma to the current offline planning track gammaplanObtaining the track gamma after on-line adjustmentonlineThe specific calculation method comprises the following steps:
Figure FDA0002881335570000034
Figure FDA0002881335570000035
wherein,
Figure FDA0002881335570000036
representing a superposition compensation operation;
8) gamma-gamma is formedonlineAs newΓplanAnd then returning to the step 3) again to perform a new round of off-line planning track adjustment until the whole metal component to be manufactured is manufactured.
2. The method of claim 1, wherein the three-dimensional topography information in step 4) is in the form of any one of a three-dimensional depth map, a three-dimensional voxel volume, a three-dimensional polygon mesh, and a three-dimensional point cloud.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107402044A (en) * 2017-07-28 2017-11-28 华中科技大学 A kind of online nondestructive detection system of metal increasing material manufacturing component quality and method
CN109894614A (en) * 2019-03-25 2019-06-18 华中科技大学 The method and its application of trajectory planning are filled on a kind of developable surface
CN110580740A (en) * 2019-08-27 2019-12-17 清华大学 multi-agent cooperative three-dimensional modeling method and device
CN110789128A (en) * 2019-10-24 2020-02-14 汕头大学 Additive manufacturing part forming quality prediction and control system and method
CN111798477A (en) * 2020-06-23 2020-10-20 西安航天精密机电研究所 Molten pool monitoring method based on visual technology

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8798840B2 (en) * 2011-09-30 2014-08-05 Irobot Corporation Adaptive mapping with spatial summaries of sensor data

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107402044A (en) * 2017-07-28 2017-11-28 华中科技大学 A kind of online nondestructive detection system of metal increasing material manufacturing component quality and method
CN109894614A (en) * 2019-03-25 2019-06-18 华中科技大学 The method and its application of trajectory planning are filled on a kind of developable surface
CN110580740A (en) * 2019-08-27 2019-12-17 清华大学 multi-agent cooperative three-dimensional modeling method and device
CN110789128A (en) * 2019-10-24 2020-02-14 汕头大学 Additive manufacturing part forming quality prediction and control system and method
CN111798477A (en) * 2020-06-23 2020-10-20 西安航天精密机电研究所 Molten pool monitoring method based on visual technology

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